Enhancing stop location detection for incomplete urban mobility datasets

• URL: http://arxiv.org/abs/2407.11579v1
• Date: Tue, 16 Jul 2024 10:41:08 GMT
• Title: Enhancing stop location detection for incomplete urban mobility datasets
• Authors: Margherita Bertè, Rashid Ibrahimli, Lars Koopmans, Pablo Valgañón, Nicola Zomer, Davide Colombi,
• Abstract summary: This study investigates the application of classification algorithms to enhance density-based methods for stop identification. Our approach incorporates multiple features, including individual routine behavior across various time and scales local characteristics of individual GPS points.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stop location detection, within human mobility studies, has an impacts in multiple fields including urban planning, transport network design, epidemiological modeling, and socio-economic segregation analysis. However, it remains a challenging task because classical density clustering algorithms often struggle with noisy or incomplete GPS datasets. This study investigates the application of classification algorithms to enhance density-based methods for stop identification. Our approach incorporates multiple features, including individual routine behavior across various time scales and local characteristics of individual GPS points. The dataset comprises privacy-preserving and anonymized GPS points previously labeled as stops by a sequence-oriented, density-dependent algorithm. We simulated data gaps by removing point density from select stops to assess performance under sparse data conditions. The model classifies individual GPS points within trajectories as potential stops or non-stops. Given the highly imbalanced nature of the dataset, we prioritized recall over precision in performance evaluation. Results indicate that this method detects most stops, even in the presence of spatio-temporal gaps and that points classified as false positives often correspond to recurring locations for devices, typically near previous stops. While this research contributes to mobility analysis techniques, significant challenges persist. The lack of ground truth data limits definitive conclusions about the algorithm's accuracy. Further research is needed to validate the method across diverse datasets and to incorporate collective behavior inputs.

Related papers

• Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios [49.1574468325115]
  We evaluate the utility of five state-of-the-art synthesis approaches in terms of real-world applicability. We focus on so-called trip data that encode fine granular urban movements such as GPS-tracked taxi rides. One model fails to produce data within reasonable time and another generates too many jumps to meet the requirements for map matching.
  arXiv  Detail & Related papers  (2024-07-03T16:08:05Z)
• Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation [51.66997548477913]
  We propose a novel feature-level consistency learning framework named Density-Descending Feature Perturbation (DDFP) Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore. The proposed DDFP outperforms other designs on feature-level perturbations and shows state of the art performances on both Pascal VOC and Cityscapes dataset.
  arXiv  Detail & Related papers  (2024-03-11T06:59:05Z)
• Physics-Guided Abnormal Trajectory Gap Detection [2.813613899641924]
  We propose a Space Time-Aware Gap Detection (STAGD) approach to leverage space-time indexing and merging trajectory gaps. We also incorporate a Dynamic Region-based Merge (DRM) approach to efficiently compute gap abnormality scores.
  arXiv  Detail & Related papers  (2024-03-10T17:07:28Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• Sequential Attention Source Identification Based on Feature Representation [88.05527934953311]
  This paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea. It's worth mentioning that the inductive learning idea ensures that TGASI can detect the sources in new scenarios without knowing other prior knowledge.
  arXiv  Detail & Related papers  (2023-06-28T03:00:28Z)
• Generative Anomaly Detection for Time Series Datasets [1.7954335118363964]
  Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. We propose a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score.
  arXiv  Detail & Related papers  (2022-06-28T17:08:47Z)
• Detecting Wandering Behavior of People with Dementia [2.071516130824992]
  We design an approach for the real-time automatic detection of wandering leading to getting lost. The approach relies on GPS data to determine frequent locations between which movement occurs. We conduct experiments on synthetic data to test the ability of the approach to find frequent locations and to compare it against an alternative, state-of-the-art approach.
  arXiv  Detail & Related papers  (2021-10-25T17:47:06Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control [55.41644538483948]
  High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research. As this technology advances, it is becoming increasingly useful also in society. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. We present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques.
  arXiv  Detail & Related papers  (2021-08-26T11:45:26Z)
• Robust GPS-Vision Localization via Integrity-Driven Landmark Attention [2.741266294612776]
  Integrity-driven Landmark Attention (ILA) for GPS-vision navigation in urban areas. Inspired by cognitive attention in humans, we perform convex optimization to select a subset of landmarks. We demonstrate improved localization accuracy and robust predicted availability for a pre-defined alert limit.
  arXiv  Detail & Related papers  (2021-01-13T02:16:13Z)
• Infostop: Scalable stop-location detection in multi-user mobility data [0.0]
  We describe the Infostop algorithm that overcomes the limitations of the state-of-the-art solution by leveraging the flow-based network community detection algorithm Infomap. We show that the size of locations detected by Infostops for increasing number of users and that time complexity grows slower than for previous solutions.
  arXiv  Detail & Related papers  (2020-03-31T17:02:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.